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http://dx.doi.org/10.9723/jksiis.2020.25.2.129

Design and Performance Analysis of ML Techniques for Finger Motion Recognition  

Jung, Woosoon (대구대학교 대학원 정보통신공학과)
Lee, Hyung Gyu (대구대학교 ICT융합학부)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.25, no.2, 2020 , pp. 129-136 More about this Journal
Abstract
Recognizing finger movements have been used as a intuitive way of human-computer interaction. In this study, we implement an wearable device for finger motion recognition and evaluate the accuracy of several ML (Machine learning) techniques. Not only HMM (Hidden markov model) and DTW (Dynamic time warping) techniques that have been traditionally used as time series data analysis, but also NN (Neural network) technique are applied to compare and analyze the accuracy of each technique. In order to minimize the computational requirement, we also apply the pre-processing to each ML techniques. Our extensive evaluations demonstrate that the NN-based gesture recognition system achieves 99.1% recognition accuracy while the HMM and DTW achieve 96.6% and 95.9% recognition accuracy, respectively.
Keywords
Gesture recognition; Flex (bend) sensor; Neural network; HMM; DTW; Time series classification (TSC);
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